Definition
Deviation Detection
Also known as outlier detection, it is the process of identifying data points that significantly differ from the majority of the data in a database.
- Deviation detection is crucial in fields like fraud detection, quality control, and network security, where identifying unusual patterns can prevent significant losses or issues.
Importance of Deviation Detection
- Error Identification: Outliers often signal data entry errors or inconsistencies that need correction.
- Anomaly Detection: In fields like finance or healthcare, outliers can indicate fraudulent transactions or abnormal patient conditions.
- Improved Decision-Making: By identifying and addressing outliers, organizations can make more accurate and reliable decisions.
- In a sales database, a single transaction showing a purchase of 10,000 units might be an outlier.
- This could either be a data entry error or an indication of bulk purchasing behavior that needs further analysis.
Steps to Implement Deviation Detection
- Data Preparation: Clean and preprocess the data to remove noise and irrelevant attributes.
- Feature Selection: Identify the most relevant attributes for outlier detection.
- Algorithm Selection: Choose an appropriate statistical, distance-based, or density-based method.
- Threshold Setting: Define thresholds for flagging outliers, such as Z-score limits or distance metrics.
- Validation: Verify the results by cross-referencing with domain experts or additional data sources.
How Deviation Detection Works
1. Statistical Techniques
- Z-Score Analysis:
- Measures how many standard deviations a data point is from the mean.
- Data points with a Z-score above a certain threshold (e.g., 3 or -3) are considered outliers.
- Interquartile Range (IQR):
- Identifies outliers by calculating the range between the first and third quartiles.
- Data points outside 1.5 times the IQR are flagged as outliers.
- In a dataset of test scores, a score of 100 in a class where the average is 70 with a standard deviation of 10 would have a Z-score of 3.
- This score would be considered an outlier.
